Farokh B. Bastani is a Professor of Computer Science at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. from the University of California, Berkeley. His research focuses on AI-driven software synthesis, embedded real-time systems, formal methods, high-assurance autonomous systems, and fault-tolerant distributed systems. He leads research in the NSF Industrial/University Cooperative Research Center (IUCRC). Education: Ph.D., Computer Science, UC Berkeley His work emphasizes software reliability, safety assurance, and modular parallel programming. Research outputs include journal and conference publications, though specific titles are not listed here. The awards section appears incomplete (404 error noted). Labs/Teams: Active involvement with the NSF IUCRC program. No advising records or grant details provided in the text.
Reza Curtmola is a Professor in the Department of Computer Science at NJIT. His research focuses on cybersecurity, distributed systems, and network security with an emphasis on secure routing, cloud computing, and privacy-preserving technologies. He holds a Ph.D. in Computer Science from Johns Hopkins University (2007), an M.S. from the same institution (2003), and a B.S. from the Politehnica University of Bucharest (2001). Dr. Curtmola’s work addresses challenges in wireless mesh networks, vehicular communication systems, and mobile-cloud integration. His contributions include innovative solutions for secure network coding, distributed resource management (e.g., parking assignment systems), and auditable data storage mechanisms. He has developed middleware frameworks like Moitree for mobile-cloud applications and has explored defenses against side-channel attacks, cache leaks, and entropy-based network vulnerabilities. His research also extends to privacy in vehicular DSRC protocols, dynamic traffic optimization, and verifiable code review systems. He has published extensively on topics ranging from cryptographic defenses in distributed systems to practical implementations of remote data checking in untrusted clouds. Current research activities include advancing secure cloud infrastructure, improving mobile crowdsensing reliability, and mitigating threats in IoT-enabled urban environments. His work often bridges theoretical foundations with practical system implementations, emphasizing real-world applicability in smart cities and critical infrastructure systems.
Dr. Min Sun is an Associate Professor and Director of the Undergraduate Program in the Department of Civil Engineering at the University of Victoria (UVic). He holds a PhD from the University of Toronto. His research focuses on structural engineering and steel structures, particularly in the areas of steel connections, seismic resilience, and numerical modeling. Dr. Sun has extensive academic and professional experience, including roles as Assistant Professor at UVic (2016–2022), Lecturer at the University of Toronto, and structural design roles in industry. His research interests emphasize the performance of steel structures under extreme loads, including earthquake engineering and material behavior. Recent work includes studies on stress concentration factors in steel connections, thermal integrity of piles, and wood-frame building reliability under lateral loads. He actively contributes to professional organizations, such as serving as Vice President (Western Region) for the Canadian Society for Civil Engineering (2018–2020). Dr. Sun teaches courses including Advanced Structural Analysis (CIVE 421) and Solid Mechanics (CIVE 220) at UVic. He currently supervises graduate students in structural steel design and construction. His publications span experimental and numerical analyses, with a focus on improving design standards for steel and wood structures. Labs/Teams: Affiliated with UVic's Engineering and Computer Science faculty and the IESVIC (Institute for Energy Systems and Sustainability at UVic), though specific lab names are not explicitly stated in the text.
Monica Maly is a Part-Time Associate Professor in Rehabilitation Science within the Faculty of Health Sciences at McMaster University. Her academic profile demonstrates extensive expertise in biomechanics and rehabilitation, with particular focus on knee osteoarthritis research. She maintains an active research program with numerous recent publications spanning rheumatology, biomechanics, and rehabilitation science. Dr. Maly's research interests center on understanding the biomechanical and physiological factors contributing to knee osteoarthritis progression and developing effective interventions. Her work examines knee joint mechanics, muscle strength and capacity, gait analysis, pain management strategies, and the impact of exercise interventions on OA symptoms. She has conducted significant research on sex differences in OA, racial disparities in pain experiences, and the relationship between obesity, inflammation, and joint function. Her methodological approaches include biomechanical analysis, clinical trials, systematic reviews, and innovative technologies like soft robotics for knee bracing. Analysis of her recent publications (2023-2025) reveals a strong focus on understanding knee osteoarthritis mechanisms through biomechanical and physiological lenses, with increasing attention to social determinants of health and health disparities. Her work spans multiple disciplines including rheumatology, biomechanics, rehabilitation science, and public health, demonstrating interdisciplinary collaboration. Key trends include examining racial disparities in pain experiences, developing novel interventions like soft robotic knee braces, and investigating the complex relationships between joint loading, biomarkers, and cartilage changes. Dr. Maly has collaborated extensively with researchers across multiple institutions, as evidenced by her numerous publications in high-impact journals such as Osteoarthritis and Cartilage, Arthritis & Rheumatology, and Clinical Biomechanics. Her work often utilizes data from large longitudinal studies including the Osteoarthritis Initiative and the Canadian Longitudinal Study on Aging. While specific grant information isn't detailed in the provided text, her extensive publication record suggests successful funding of multiple research projects.
Professor Bruce N. Walker holds a joint appointment in the School of Psychology and School of Interactive Computing at Georgia Institute of Technology, within the College of Sciences. His research focuses on human-centered technology design, emphasizing accessibility, auditory displays, and human-AI interaction. He leads the Sonification Lab, pioneering multimodal interfaces and inclusive technology solutions. He earned his Ph.D. in Human Factors and Human-Computer Interaction from Rice University in 2001. Research Interests: Trust in technology, accessible interfaces, sonification, AI-human collaboration, and HCI in non-traditional environments. Current projects include the AccessCORPS VIP initiative to enhance course accessibility and studies on automated vehicle interaction. Awards: Best Paper Award at AudioMostly 2014 for auditory weather reports research. Active in professional organizations like the International Community for Auditory Display and Human Factors and Ergonomics Society. Teaching: Courses include Research Methods for Human Factors, Sensation and Perception, and HCI Foundations. Supervises interdisciplinary teams in the Sonification Lab R&D Studio and AccessCORPS VIP. Labs/Teams: Sonification Lab (multimodal data exploration) and AccessCORPS (disability-inclusive course design). Collaborates on international projects like the Mwangaza initiative for learners with vision impairment in Kenya.
Laura Bruckman is a Climo Associate Professor in the Department of Materials Science and Engineering at Case Western Reserve University's Case School of Engineering. Her research focuses on predictive lifetime modeling for materials degradation, quantitative spectroscopic characterization of materials, and applying statistical analytics and data science to solve challenges in photovoltaic systems and long-lived engineering materials. Her work emphasizes understanding degradation mechanisms in photovoltaic materials (e.g., backsheets, encapsulants, and silicon cells) under environmental stressors, with applications in improving reliability and service life through advanced data-driven approaches. Dr. Bruckman has contributed to the development of machine learning methods for material characterization (e.g., ToF-SIMS analysis) and spatiotemporal models for predicting degradation patterns in field-deployed PV systems. Her research also extends to curriculum design for applied data science, emphasizing industry-relevant training in statistical modeling and interdisciplinary problem-solving. Her expertise bridges materials science, data science, and energy systems, with over 50 peer-reviewed publications and a patent in classification using multivariate optical computing. Key technical contributions include analyzing environmental impacts on solar module performance, quantifying crack propagation in polymers, and developing predictive frameworks for material aging. Her work has been supported by collaborations with industry partners and federal research initiatives.
Pablo Aragón is a Research Scientist at the Wikimedia Foundation and an Adjunct Professor at Universitat Pompeu Fabra. His work bridges computational social science, civic technology, and technopolitics, with a focus on Wikipedia's governance, digital democracy tools, and participatory systems. He co-founded the Democratic Innovation Lab in Barcelona and the DatAnalysis15M research network. Key research interests include analyzing knowledge integrity in Wikipedia, configuring digital participatory budgeting systems, and studying platform effects in civic technologies. He has led projects like DECODE (decentralized citizen engagement) and contributed to platforms like Decidim, which empower participatory democracy in cities like Barcelona. Recent conference engagements include KDD 2024 (data mining), ICWSM 2024 (social media analysis), and Wikimedia CEE Meeting 2024. His work emphasizes cross-cultural collaboration, with studies published in ACM Transactions on Computer-Human Interaction and peer-reviewed conferences like CIKM and ACM SIGKDD. Professional affiliations include the Decidim association, Amnistía Internacional España, and the open knowledge advocacy group Civio. His research often intersects with open science, free culture movements, and gender equity in urban mobility.
Refik Soyer is a Professor of Statistics at The George Washington University. His research focuses on Bayesian statistics, reliability modeling, decision analysis, and time series analysis. He has made significant contributions to the application of Bayesian methods in reliability engineering, queueing systems, and adversarial risk analysis. Education: D. Sc. in Statistics (1985), George Washington University His recent publications highlight advancements in Bayesian reliability analysis, adversarial decision frameworks, and computational methods for time series and queueing systems. Areas of emphasis include dynamic INAR processes, accelerated life testing, and software failure modeling. Soyer's work bridges theoretical statistics with practical applications in call centers, healthcare fraud detection, and risk management.
Xi Zhang is a Full Professor in the Department of Electrical and Computer Engineering at Texas A&M University . He is also the Founding Director of the Networking and Information Systems Laboratory. His academic career includes research fellowships at the University of Technology Sydney and James Cook University, as well as prior roles at AT&T Bell Laboratories and AT&T Laboratories Research. Education: B.S. and M.S. in Electrical Engineering & Computer Science, Xidian University, China M.S. in Electrical Engineering & Computer Science, Lehigh University, USA Ph.D. in Electrical Engineering-Systems, University of Michigan, USA Research Interests: His work focuses on Quality-of-Service (QoS) theory, 6G/Next-Generation Wireless Networks , Massive MIMO , Integrated Sensing and Communications (ISAC) , and Network Function Virtualization (NFV) . He has pioneered advancements in statistical delay/error-rate bounded QoS , AI-driven 6G architectures , and mURLLC (massive ultra-reliable low-latency communications) . Awards & Honors: IEEE Fellow (2014) for contributions to QoS theory in mobile wireless networks NSF Early Career Award (2004) Multiple Best Paper Awards (IEEE GLOBECOM, WCNC, ICC) Outstanding Faculty Award from Texas A&M (2020) Leadership Roles: He has held key positions as Technical Program Committee (TPC) Chair for major conferences (e.g., IEEE GLOBECOM 2011, IEEE ICDCS 2026) and serves as Editor for top-tier journals like IEEE Transactions on Communications and IEEE Transactions on Wireless Communications . Labs & Teams: He leads the Networking and Information Systems Laboratory , focusing on 6G mobile networks, ISAC systems, and AI-driven network architectures.
Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University (MSU), leading the Heterogeneous Learning and Reasoning (HLR) Lab. Her research focuses on Neuro-Symbolic AI, spatial language understanding, and structured learning, with notable contributions to frameworks like Saul for declarative programming. She joined MSU in 2019 after roles at Tulane University and the Florida Institute for Human and Machine Cognition. Education: Ph.D. in Computer Science from KU Leuven (2013), postdoctoral research at UIUC's Cognitive Computation Group, and work in the KnowEng project. Research Interests: Artificial Intelligence, Machine Learning, Natural Language Processing, Neuro-Symbolic systems, spatial semantics extraction, structured output learning, and multimodal reasoning. Key projects include NSF CAREER awards for spatial language understanding and ONR grants for integrating domain knowledge into AI. Awards: NSF CAREER (2019), Amazon Faculty Research Award (2022), Fulbright Scholar (2025), and Rising Stars at MIT EECS (2015). Grants: Active projects on Neuro-Symbolic compositional generalization (ONR), spatial language learning (NSF), and collaborations with the Department of Media and Information for health misinformation management. Professional Activities: Editorial roles at JAIR, TACL, and Frontiers journals; service on program committees for ACL, EMNLP, and AAAI; organization of workshops like Spatial Language Understanding (SpLU) and CLeaR. Lab and Software: HLR Lab develops Saul (declarative learning-based programming framework) and tools for spatial role labeling. Her team emphasizes mentoring, with structured weekly meetings, reading groups, and conference participation for students.
Dr. Gunel Jahangirova is a Lecturer in Computer Science within the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences at King's College London. Her research focuses on software testing, software engineering for AI, and search-based software engineering. She earned her PhD through a joint program at Fondazione Bruno Kessler (Italy) and University College London (UK), followed by postdoctoral work on the ERC-funded 'Precrime' project at Università della Svizzera italiana (Switzerland). Software Testing AI Engineering Search-Based Optimization Deep Learning Verification Her recent publications explore fault localization in neural networks, ethical testing of autonomous systems, and environmental impacts of AI code development. Current projects include ITEA GENIUS and ITEA GreenCode , focusing on AI testing and sustainable software practices.
Olivia Cheung is an Assistant Professor of Psychology and Global Network Assistant Professor at New York University Abu Dhabi (NYUAD), affiliated with the Division of Science and the Department of Psychology. She leads the Objects and Knowledge Laboratory (OAK Lab), which is also associated with the Center for Brain and Health at NYUAD. Education: BSSc, Chinese University of Hong Kong PhD, Vanderbilt University Postdoctoral Training: Harvard Medical School, CIMeC (Trento, Italy), Harvard University Her research focuses on cognitive neuroscience and visual cognition, particularly how experience and learning shape perception. She investigates how visual and conceptual knowledge interact to influence representations of objects, faces, words, musical notations, and scenes. Her lab employs behavioral experiments, functional magnetic resonance imaging (fMRI), and computational modeling to explore perceptual expertise and category selectivity in the brain. Her recent publications (2022–2024) reveal a consistent focus on high-level vision, with studies on holistic face and word processing, neural and computational models of category recognition, ensemble perception of animacy, and social judgments from faces (e.g., election prediction). These works, presented at Vision Sciences Society (VSS), demonstrate interdisciplinary methods and collaborations with students and international researchers. Olivia Cheung teaches courses such as Capstone Projects in Computer Science and Psychology, and Concepts and Categories: How We Structure the World , reflecting her interdisciplinary approach. She mentors undergraduate researchers, many of whom have co-authored conference posters. Her lab, the OAK Lab, fosters research on the intersection of perception, knowledge, and expertise.
I. Safak Bayram is a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Glasgow, UK. He joined Strathclyde in 2020 as a Chancellor's Fellow, following his role as an Assistant Professor and Scientist at Hamad Bin Khalifa University, Qatar. His research focuses on advancing sustainability and efficiency in intelligent power grids and transportation networks through system-level modeling, control, and management frameworks. Education: PhD in Electrical and Computer Engineering, North Carolina State University (2014) MSc in Telecommunications, University of Pittsburgh (2010) BSc in Electrical and Electronics Engineering, Dokuz Eylul University, Turkey (2007) His research interests center on the integration of electric vehicles (EVs), renewable energy, and energy storage systems into the grid to decarbonize transportation and electricity sectors. He specializes in smart charging, demand-side management, harmonics, power quality, and V2G technologies. His recent publications (2024–2025) emphasize experimental and modeling approaches to EV smart charging impacts on transformers, phase imbalance, and grid compatibility, reflecting a strong focus on real-world deployment and grid resilience. Scientific Awards: Best Paper Award, IEEE SmartGridComm (2024) Best Paper Award, IEEE Workshop on Renewable Energy and Smart Grid (2015) Best Paper Award, IEEE SmartGridComm (2018) Best Readings in Smart Grid Communications (2014) Adjunct Faculty Member Appointment (2020) Dr. Bayram is actively involved in research leadership and academic service. He has secured multiple research grants as Principal Investigator, including projects on V2G hubs and off-grid EV charging. He serves as an Associate Editor for IEEE Transactions on Transportation Electrification and IET Electrical Systems in Transportation, and has organized special issues and conferences such as IEEE SmartGridComm. He regularly delivers tutorials and participates in international conferences, contributing to the global smart grid and electrification community. Labs and Research Teams: His work is supported by active collaborations with industry (e.g., Arnold Clark Automobiles Limited) and research institutions. He leads research on modular EV charging (BumblebeeEV), smart charging algorithms, and grid integration projects, often involving experimental validation and field data analysis.
Martin Nordal Petersen is an Associate Professor at the Department of Electrical and Photonics Engineering , Technical University of Denmark (DTU) . His work spans Internet of Things (IoT) , optical networking , and wireless communication systems, with notable contributions to LoRa , NB-IoT , and LPWAN technologies. He actively supervises PhD projects on topics such as machine learning in IoT edge devices , secure 5G communication , and smart community architectures . Active projects (2024–2027): Machine Learning in IoT Edge Devices , Deterministic and Secure 5G Communication Finished projects (2021–2024; 2018–2021; 2015–2018): Reliable M2M/IoT Communication , Smart Communities , IoT 100% , Network Slicing His research explores: IoT Reliability : Multi-RAT communication, backup systems, and signal propagation Optical Networks : Alien wavelength integration, SDN control, and network emulation platforms Wireless Innovation : GPS-free geolocation, maritime NB-IoT use cases, and multimode fiber distribution Current collaborations emphasize cross-disciplinary applications of IoT in healthcare , industrial ergonomics , and smart environments .
Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.
